OpenRaiser/Intern-Atlas huggingface dataset:
Enabling MPI communication within Numba/LLVM JIT-compiled Python code using numba-mpi v1.0
Venue: arXiv cs.DC 2024
Year: 2024

The numba-mpi package offers access to the Message Passing Interface (MPI) routines from Python code that uses the Numba just-in-time (JIT) compiler. As a result, high-performance and multi-threaded Python code may utilize MPI communication facilities without leaving the JIT-compiled code blocks, which is not possible with the mpi4py package, a higher-level Python interface to MPI. For debugging purposes, numba-mpi retains full functionality of the code even if the JIT compilation is disabled. The numba-mpi API constitutes a thin wrapper around the C API of MPI and is built around Numpy arrays including handling of non-contiguous views over array slices. Project development is hosted at GitHub leveraging the mpi4py/setup-mpi workflow enabling continuous integration tests on Linux (MPICH, OpenMPI & Intel MPI), macOS (MPICH & OpenMPI) and Windows (MS MPI). The paper covers an overview of the package features, architecture and performance. As of v1.0, the following MPI routines are exposed and covered by unit tests: size/rank, [i]send/[i]recv, wait[all|any], test[all|any], allreduce, bcast, barrier, scatter/[all]gather & wtime. The package is implemented in pure Python and depends on numpy, numba and mpi4py (the latter used at initialization and as a source of utility routines only). The performance advantage of using numba-mpi compared to mpi4py is depicted with a simple example, with entirety of the code included in listings discussed in the text. Application of numba-mpi for handling domain decomposition in numerical solvers for partial differential equations is presented using two external packages that depend on numba-mpi: py-pde and PyMPDATA-MPI.
Forecasting GPU Performance for Deep Learning Training and Inference
Venue: arXiv cs.LG 2024
Year: 2024

Deep learning kernels exhibit predictable memory accesses and compute patterns, making GPUs' parallel architecture well-suited for their execution. Software and runtime systems for GPUs are optimized to better utilize the stream multiprocessors, on-chip cache, and off-chip high-bandwidth memory. As deep learning models and GPUs evolve, access to newer GPUs is often limited, raising questions about the performance of new model architectures on existing GPUs, existing models on new GPUs, and new model architectures on new GPUs. To address these questions, we introduce NeuSight, a framework to predict the performance of various deep learning models, for both training and inference, on unseen GPUs without requiring actual execution. The framework leverages both GPU hardware behavior and software library optimizations to estimate end-to-end performance. Previous work uses regression models that capture linear trends or multilayer perceptrons to predict the overall latency of deep learning kernels on GPUs. These approaches suffer from higher error percentages when forecasting performance on unseen models and new GPUs. Instead, NeuSight decomposes the prediction problem into smaller problems, bounding the prediction through fundamental performance laws. NeuSight decomposes a single deep learning kernel prediction into smaller working sets called tiles, which are executed independently on the GPU. Tile-granularity predictions are determined using a machine learning approach and aggregated to estimate end-to-end latency. NeuSight outperforms prior work across various deep learning workloads and the latest GPUs. It reduces the percentage error from 121.4% and 30.8% to 2.3% in predicting the latency of GPT3 model for training and inference on H100, compared to state-of-the-art prior work, where both GPT3 and H100 were not used to train the framework.
Network Traffic Analysis of Medical Devices
Venue: arXiv cs.CR 2024
Year: 2024

The availability of medical devices such as glucose levels and blood pressure measuring devices is continuously increasing. These devices have gained user interest as they are easy to use. However, medical devices introduce extra complexity to the network by being numerous, heterogeneous, and more vulnerable to cyber-attacks. For better network management and overall network security, it is important to understand the network traffic characteristics of the devices. Thus, in this paper, we analyze in detail the traffic characteristics of 8 medical devices both at the device level and at the level of individual functionality of each device. We collect and share both network and Bluetooth traffic from a total of 51 functionalities of the devices. Our analysis includes different metrics such as protocols, amount of incoming/outgoing traffic, DNS queries, and analysis of traffic destinations. We observed that devices have unique network and Bluetooth traffic characteristics, that might be useful in developing networking tools for medical devices.
RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
Venue: arXiv cs.CL 2024
Year: 2024

Question answering based on retrieval augmented generation (RAG-QA) is an important research topic in NLP and has a wide range of real-world applications. However, most existing datasets for this task are either constructed using a single source corpus or consist of short extractive answers, which fall short of evaluating large language model (LLM) based RAG-QA systems on cross-domain generalization. To address these limitations, we create Long-form RobustQA (LFRQA), a new dataset comprising human-written long-form answers that integrate short extractive answers from multiple documents into a single, coherent narrative, covering 26K queries and large corpora across seven different domains. We further propose RAG-QA Arena by directly comparing model-generated answers against LFRQA's answers using LLMs as evaluators. We show via extensive experiments that RAG-QA Arena and human judgments on answer quality are highly correlated. Moreover, only 41.3% of the most competitive LLM's answers are preferred to LFRQA's answers, demonstrating RAG-QA Arena as a challenging evaluation platform for future research.
Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance
Venue: arXiv cs.CV 2024
Year: 2024

Remote sensing image change captioning (RSICC) aims to articulate the changes in objects of interest within bi-temporal remote sensing images using natural language. Given the limitations of current RSICC methods in expressing general features across multi-temporal and spatial scenarios, and their deficiency in providing granular, robust, and precise change descriptions, we introduce a novel change captioning (CC) method based on the foundational knowledge and semantic guidance, which we term Semantic-CC. Semantic-CC alleviates the dependency of high-generalization algorithms on extensive annotations by harnessing the latent knowledge of foundation models, and it generates more comprehensive and accurate change descriptions guided by pixel-level semantics from change detection (CD). Specifically, we propose a bi-temporal SAM-based encoder for dual-image feature extraction; a multi-task semantic aggregation neck for facilitating information interaction between heterogeneous tasks; a straightforward multi-scale change detection decoder to provide pixel-level semantic guidance; and a change caption decoder based on the large language model (LLM) to generate change description sentences. Moreover, to ensure the stability of the joint training of CD and CC, we propose a three-stage training strategy that supervises different tasks at various stages. We validate the proposed method on the LEVIR-CC and LEVIR-CD datasets. The experimental results corroborate the complementarity of CD and CC, demonstrating that Semantic-CC can generate more accurate change descriptions and achieve optimal performance across both tasks.
Data Guards: Challenges and Solutions for Fostering Trust in Data
Venue: arXiv cs.HC 2024
Year: 2024

From dirty data to intentional deception, there are many threats to the validity of data-driven decisions. Making use of data, especially new or unfamiliar data, therefore requires a degree of trust or verification. How is this trust established? In this paper, we present the results of a series of interviews with both producers and consumers of data artifacts (outputs of data ecosystems like spreadsheets, charts, and dashboards) aimed at understanding strategies and obstacles to building trust in data. We find a recurring need, but lack of existing standards, for data validation and verification, especially among data consumers. We therefore propose a set of data guards: methods and tools for fostering trust in data artifacts.
Kinematics-based 3D Human-Object Interaction Reconstruction from Single View
Venue: arXiv cs.CV 2024
Year: 2024

Reconstructing 3D human-object interaction (HOI) from single-view RGB images is challenging due to the absence of depth information and potential occlusions. Existing methods simply predict the body poses merely rely on network training on some indoor datasets, which cannot guarantee the rationality of the results if some body parts are invisible due to occlusions that appear easily. Inspired by the end-effector localization task in robotics, we propose a kinematics-based method that can drive the joints of human body to the human-object contact regions accurately. After an improved forward kinematics algorithm is proposed, the Multi-Layer Perceptron is introduced into the solution of inverse kinematics process to determine the poses of joints, which achieves precise results than the commonly-used numerical methods in robotics. Besides, a Contact Region Recognition Network (CRRNet) is also proposed to robustly determine the contact regions using a single-view video. Experimental results demonstrate that our method outperforms the state-of-the-art on benchmark BEHAVE. Additionally, our approach shows good portability and can be seamlessly integrated into other methods for optimizations.
DirectL: Efficient Radiance Fields Rendering for 3D Light Field Displays
Venue: arXiv cs.GR 2024
Year: 2024

Autostereoscopic display, despite decades of development, has not achieved extensive application, primarily due to the daunting challenge of 3D content creation for non-specialists. The emergence of Radiance Field as an innovative 3D representation has markedly revolutionized the domains of 3D reconstruction and generation. This technology greatly simplifies 3D content creation for common users, broadening the applicability of Light Field Displays (LFDs). However, the combination of these two fields remains largely unexplored. The standard paradigm to create optimal content for parallax-based light field displays demands rendering at least 45 slightly shifted views preferably at high resolution per frame, a substantial hurdle for real-time rendering. We introduce DirectL, a novel rendering paradigm for Radiance Fields on 3D displays. We thoroughly analyze the interweaved mapping of spatial rays to screen subpixels, precisely determine the light rays entering the human eye, and propose subpixel repurposing to significantly reduce the pixel count required for rendering. Tailored for the two predominant radiance fields--Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS), we propose corresponding optimized rendering pipelines that directly render the light field images instead of multi-view images. Extensive experiments across various displays and user study demonstrate that DirectL accelerates rendering by up to 40 times compared to the standard paradigm without sacrificing visual quality. Its rendering process-only modification allows seamless integration into subsequent radiance field tasks. Finally, we integrate DirectL into diverse applications, showcasing the stunning visual experiences and the synergy between LFDs and Radiance Fields, which unveils tremendous potential for commercialization applications. \href{direct-l.github.io}{\textbf{Project Homepage}
Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Field
Venue: arXiv cs.LG 2024
Year: 2024

There are many cases where LLMs are used for specific tasks in a single domain. These usually require less general, but more domain-specific knowledge. Highly capable, general-purpose state-of-the-art language models like GPT-4 or Claude-3-opus can often be used for such tasks, but they are very large and cannot be run locally, even if they were not proprietary. This can be a problem when working with sensitive data. This paper focuses on domain-specific and mixed-domain pretraining as potentially more efficient methods than general pretraining for specialized language models. We will take a look at work related to domain-specific pretraining, specifically in the medical area, and compare benchmark results of specialized language models to general-purpose language models.
Score Normalization for Demographic Fairness in Face Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Fair biometric algorithms have similar verification performance across different demographic groups given a single decision threshold. Unfortunately, for state-of-the-art face recognition networks, score distributions differ between demographics. Contrary to work that tries to align those distributions by extra training or fine-tuning, we solely focus on score post-processing methods. As proved, well-known sample-centered score normalization techniques, Z-norm and T-norm, do not improve fairness for high-security operating points. Thus, we extend the standard Z/T-norm to integrate demographic information in normalization. Additionally, we investigate several possibilities to incorporate cohort similarities for both genuine and impostor pairs per demographic to improve fairness across different operating points. We run experiments on two datasets with different demographics (gender and ethnicity) and show that our techniques generally improve the overall fairness of five state-of-the-art pre-trained face recognition networks, without downgrading verification performance. We also indicate that an equal contribution of False Match Rate (FMR) and False Non-Match Rate (FNMR) in fairness evaluation is required for the highest gains. Code and protocols are available.
Zero-Shot Underwater Gesture Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Hand gesture recognition allows humans to interact with machines non-verbally, which has a huge application in underwater exploration using autonomous underwater vehicles. Recently, a new gesture-based language called CADDIAN has been devised for divers, and supervised learning methods have been applied to recognize the gestures with high accuracy. However, such methods fail when they encounter unseen gestures in real time. In this work, we advocate the need for zero-shot underwater gesture recognition (ZSUGR), where the objective is to train a model with visual samples of gestures from a few ``seen'' classes only and transfer the gained knowledge at test time to recognize semantically-similar unseen gesture classes as well. After discussing the problem and dataset-specific challenges, we propose new seen-unseen splits for gesture classes in CADDY dataset. Then, we present a two-stage framework, where a novel transformer learns strong visual gesture cues and feeds them to a conditional generative adversarial network that learns to mimic feature distribution. We use the trained generator as a feature synthesizer for unseen classes, enabling zero-shot learning. Extensive experiments demonstrate that our method outperforms the existing zero-shot techniques. We conclude by providing useful insights into our framework and suggesting directions for future research.
Seismic Fault SAM: Adapting SAM with Lightweight Modules and 2.5D Strategy for Fault Detection
Venue: arXiv cs.CV 2024
Year: 2024

Seismic fault detection holds significant geographical and practical application value, aiding experts in subsurface structure interpretation and resource exploration. Despite some progress made by automated methods based on deep learning, research in the seismic domain faces significant challenges, particularly because it is difficult to obtain high-quality, large-scale, open-source, and diverse datasets, which hinders the development of general foundation models. Therefore, this paper proposes Seismic Fault SAM, which, for the first time, applies the general pre-training foundation model-Segment Anything Model (SAM)-to seismic fault interpretation. This method aligns the universal knowledge learned from a vast amount of images with the seismic domain tasks through an Adapter design. Specifically, our innovative points include designing lightweight Adapter modules, freezing most of the pre-training weights, and only updating a small number of parameters to allow the model to converge quickly and effectively learn fault features; combining 2.5D input strategy to capture 3D spatial patterns with 2D models; integrating geological constraints into the model through prior-based data augmentation techniques to enhance the model's generalization capability. Experimental results on the largest publicly available seismic dataset, Thebe, show that our method surpasses existing 3D models on both OIS and ODS metrics, achieving state-of-the-art performance and providing an effective extension scheme for other seismic domain downstream tasks that lack labeled data.
I Know About "Up"! Enhancing Spatial Reasoning in Visual Language Models Through 3D Reconstruction
Venue: arXiv cs.CL 2024
Year: 2024

Visual Language Models (VLMs) are essential for various tasks, particularly visual reasoning tasks, due to their robust multi-modal information integration, visual reasoning capabilities, and contextual awareness. However, existing \VLMs{}' visual spatial reasoning capabilities are often inadequate, struggling even with basic tasks such as distinguishing left from right. To address this, we propose the \ours{} model, designed to enhance the visual spatial reasoning abilities of VLMS. ZeroVLM employs Zero-1-to-3, a 3D reconstruction model for obtaining different views of the input images and incorporates a prompting mechanism to further improve visual spatial reasoning. Experimental results on four visual spatial reasoning datasets show that our \ours{} achieves up to 19.48% accuracy improvement, which indicates the effectiveness of the 3D reconstruction and prompting mechanisms of our ZeroVLM.
On the Complexity of Reachability Properties in Serverless Function Scheduling
Venue: arXiv cs.DC 2024
Year: 2024

Functions-as-a-Service (FaaS) is a Serverless Cloud paradigm where a platform manages the execution scheduling (e.g., resource allocation, runtime environments) of stateless functions. Recent developments demonstrate the benefits of using domain-specific languages to express per-function scheduling policies, e.g., enforcing the allocation of functions on nodes that enjoy low data-access latencies thanks to proximity and connection pooling. We present aAPP, an affinity-aware extension of a platform-agnostic function scheduling language. We formalise its scheduling semantics and then study the complexity of statically checking reachability properties, e.g., useful to verify that trusted and untrusted functions cannot be co-located. Analysing different fragments of aAPP, we show that checking reachability of policies without affinity has linear complexity, while affinity makes the problem PSpace.
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models
Venue: arXiv cs.LG 2024
Year: 2024

In the drug discovery process, where experiments can be costly and time-consuming, computational models that predict drug-target interactions are valuable tools to accelerate the development of new therapeutic agents. Estimating the uncertainty inherent in these neural network predictions provides valuable information that facilitates optimal decision-making when risk assessment is crucial. However, such models can be poorly calibrated, which results in unreliable uncertainty estimates that do not reflect the true predictive uncertainty. In this study, we compare different metrics, including accuracy and calibration scores, used for model hyperparameter tuning to investigate which model selection strategy achieves well-calibrated models. Furthermore, we propose to use a computationally efficient Bayesian uncertainty estimation method named Bayesian Linear Probing (BLP), which generates Hamiltonian Monte Carlo (HMC) trajectories to obtain samples for the parameters of a Bayesian Logistic Regression fitted to the hidden layer of the baseline neural network. We report that BLP improves model calibration and achieves the performance of common uncertainty quantification methods by combining the benefits of uncertainty estimation and probability calibration methods. Finally, we show that combining post hoc calibration method with well-performing uncertainty quantification approaches can boost model accuracy and calibration.
Normative Diffusion Autoencoders: Application to Amyotrophic Lateral Sclerosis
Venue: arXiv cs.CV 2024
Year: 2024

Predicting survival in Amyotrophic Lateral Sclerosis (ALS) is a challenging task. Magnetic resonance imaging (MRI) data provide in vivo insight into brain health, but the low prevalence of the condition and resultant data scarcity limit training set sizes for prediction models. Survival models are further hindered by the subtle and often highly localised profile of ALS-related neurodegeneration. Normative models present a solution as they increase statistical power by leveraging large healthy cohorts. Separately, diffusion models excel in capturing the semantics embedded within images including subtle signs of accelerated brain ageing, which may help predict survival in ALS. Here, we combine the benefits of generative and normative modelling by introducing the normative diffusion autoencoder framework. To our knowledge, this is the first use of normative modelling within a diffusion autoencoder, as well as the first application of normative modelling to ALS. Our approach outperforms generative and non-generative normative modelling benchmarks in ALS prognostication, demonstrating enhanced predictive accuracy in the context of ALS survival prediction and normative modelling in general.
Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reduction
Venue: arXiv cs.LG 2024
Year: 2024

One of the key issues regarding classification problems in Trustworthy Artificial Intelligence is ensuring Fairness in the prediction of different classes when protected (sensitive) features are present. Data quality is critical in these cases, as biases in training data can be reflected in machine learning, impacting human lives and failing to comply with current regulations. One strategy to improve data quality and avoid these problems is preprocessing the dataset. Instance selection via undersampling can foster balanced learning of classes and protected feature values. Performing undersampling in class overlap areas close to the decision boundary should bolster the impact on the classifier. This work proposes Fair Overlap Number of Balls (Fair-ONB), an undersampling method that harnesses the data morphology of the different data groups (obtained from the combination of classes and protected feature values) to perform guided undersampling in overlap areas. It employs attributes of the ball coverage of the groups, such as the radius, number of covered instances and density, to select the most suitable areas for undersampling and reduce bias. Results show that the Fair-ONB method improves model Fairness with low impact on the classifier's predictive performance.